Intern, Early Translational Data Science (Project: Bayesian Modelling of Preclinical Oncology Data)

At Genmab, we are dedicated to building extra[not]ordinary® futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines® that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals’ unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees.

Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so.

Does this inspire you and feel like a fit? Then we would love to have you join us!

Interested in more than one Translational Data Science internship?

We are posting several projects within this department, each with its own research focus, recruitment team, and selection process. Please take time to explore the available projects and apply to a maximum of two that best match your interests and skills.

If you apply to two, include the project titles in order of preference in both motivation letters, with a brief explanation of your interest in each. This helps our teams understand your preferences throughout the separate selection processes!

The Role

Are you excited about the intersection of statistics and preclinical oncology, looking to make a meaningful impact on how efficacy data from rodent readouts are analyzed and reused across studies? Do you want to gain exposure to hierarchical and Bayesian modeling approaches applied to real, accumulated preclinical data? Keep on reading!

We are seeking a highly motivated intern to join the Early Translational Data Science team, contributing to a project that assembles and models retrospective tumor efficacy data across multiple preclinical models and studies. This role will focus on applying hierarchical and Bayesian statistical methods to historical control-arm data, evaluating whether it can be formally reused ("borrowed") in future studies to reduce control group sizes while preserving statistical power. This is an exciting, multidisciplinary project that combines Data Science, Biostatistics and in vivo Pharmacology, and directly supports Genmab's 3Rs (Replacement, Reduction, Refinement) commitments.

Alongside the analytical work, you will be part of a multidisciplinary team where discussing ideas, asking questions, and learning from each other are part of the research process. We are looking for someone who is curious about both the data and the people behind the research, and who enjoys exploring how their findings can help colleagues approach scientific questions.

Responsibilities

• Assemble and harmonize retrospective control and treatment arm data across multiple tumor models and studies.

• Build reproducible analysis pipelines (R, notebooks) to visualize and decompose variance in control-arm trajectories, distinguishing animal-to-animal from study-to-study variability.

• Classify tumor models as candidates for historical control borrowing based on cross-study variance, and apply meta-regression to identify biological/experimental drivers of variability.

• Fit and validate hierarchical (meta-analytic-predictive) Bayesian models on historical control-arm data, including leave-one-study-out backtesting.

• Simulate reduced control group size scenarios to generate defensible, model-specific sample-size recommendations.

• Collaborate cross-functionally with scientists in Non-Clinical Safety (NCS) and Pre-clinical in vivo pharmacology (PiP).

Requirements

• Currently enrolled in a Master's degree program at a Dutch university in Biostatistics, Bioinformatics, Biomedical Sciences, Data Science or a related field.

• Strong statistical expertise, demonstrated through coursework and practical research experience, ideally gained during a previous internship. This project is particularly suited to students seeking their second Master’s internship.

• Proficiency in R and/or Python programming, with a focus on producing clean, standardized, and well-documented code.
• Affinity with biological data is required.
• Familiarity with mixed-effects or hierarchical modeling concepts and handling and manipulating large-scale, multi-study datasets.

• Familiarity with version control (e.g. Git).

• Experience working with preclinical or clinical study data (e.g., in vivo efficacy studies, tumor growth data) is a plus.

• Strong knowledge of statistical modelling (e.g., Bayesian models); familiarity with meta-analysis in oncology pharmacology is a plus.
• Enjoy working with colleagues from different scientific backgrounds, sharing your perspective and taking an interest in theirs.
• Bring curiosity, analytical thinking, and a willingness to ask questions, discuss challenges, and learn from feedback.
• Communicate clearly in English and enjoy making complex findings understandable to others.

General Information

• Internship duration: 9 months (a minimum of 8 months is required; please only apply if you can commit to this duration).

• Start date: 1 February or 1 March (fixed start dates aligned with our internship cohorts, so you can join fellow students for onboarding and make the most of the shared learning activities throughout your internship).

• Location: Utrecht, Netherlands

• Working arrangement: Full-time, hybrid, with three days onsite and two days working remotely.

The proposed gross annual/monthly base salary range for this position, in the primary location, based on a full time schedule is:

EUR750,00---750,00

The final salary offer will depend on several factors, including your skills, qualifications, and experience.

In addition to base salary, this position is eligible for additional forms of compensation, such as discretionary bonuses and long-term incentives.

When you join Genmab, you become a part of a culture that supports your physical, financial, social, and emotional well-being. Our benefits include, but are not limited to:

• Pension
• Health insurance and wellness benefits
• Paid time off
• Employee support programs

Further details on eligibility for compensation and benefits based on the role will be provided during the recruitment process.

About Genmab

Genmab is an international biotechnology company with a core purpose to improve the lives of patients through innovative and differentiated antibody therapeutics. For 25 years, its hard-working, innovative and collaborative team has invented next-generation antibody technology platforms and harnessed translational, quantitative and data sciences, resulting in a proprietary pipeline including bispecific T-cell engagers, antibody-drug conjugates, next-generation immune checkpoint modulators and effector function-enhanced antibodies. By 2030, Genmab’s vision is to transform the lives of people with cancer and other serious diseases with Knock-Your-Socks-Off (KYSO®) antibody medicines.

Established in 1999, Genmab is headquartered in Copenhagen, Denmark with international presence across North America, Europe and Asia Pacific. For more information, please visit Genmab.com and follow us on LinkedIn and X.

Genmab is committed to protecting your personal data and privacy. Please see our privacy policy for handling your data in connection with your application on our website Job Applicant Privacy Notice (genmab.com).

Please note that if you are applying for a position in the Netherlands, Genmab’s policy for all permanently budgeted hires in NL is initially to offer a fixed-term employment contract for a year, if the employee performs well and if the business conditions do not change, renewal for an indefinite term may be considered after the contract.